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This, I have learned, is a shallow sentence that can be repeated and repeated and never provides the solace, comfort or answers we seek when their times come.
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It's "Shallow" time!
The problem of RE is treated as the problem of pair-of-entities classification: examples consisted of parts of sentence shallow parse trees, where relation roles were identified by tree attributes, such as member or affiliation relation.
Shallow representation named partial sentence trees are employed to represent automatic speech recognition outputs.
The input to the system is a shallow, possibly under-specified sentence representation similar to the specification considered in the Surface Realisation Shared Task [12].
This is realized via an efficient pattern matching approach that uses manually crafted rules over the shallow structure of the sentence.
For instance, ReVerb [7] and OLLIE [26] assign to each extraction a confidence score using a logistic classifier trained on random Web sentences with shallow syntactic features and ClausIE [27] takes the confidence score of the dependency parses generated by the Stanford parser as the confidence score of each extraction.
The authors deployed various natural language processing techniques (shallow parsing, automatic term extraction, sentence transformation, and computing of semantic distance).
To classify the relation instances, we used a kernel method based only on shallow linguistic information of the sentences.
They applied several NLP techniques like term extraction, shallow parsing, computation of semantic distance and sentence transformation for the task.
The shallow linguistic kernel recalled 70.1% of the sentence-level connectivity statements at 50.3% precision.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com